Home health optimization

ABSTRACT

The present invention comprises a novel Virtual Facilities Manager architecture that optimizes home health by combining multiple prediction engines, fed by continuously monitored and processed sensor-based and environmental data (historical as well as current), with an integrated Homecare Network of homeowners and service and other providers. This enables a continuous feedback process in which abnormal conditions (symptoms of an underlying problem, including suboptimal performance) are detected and addressed by issuing contextual alerts with associated actions (related alert-action pairs) in an iterative manner over time. By maintaining a Home Health Record including Scores (e.g., reliability and energy efficiency) representing the systemic state of one or more homes, the present invention detects not only maintenance issues, but also suboptimal performance “problems,” and addresses them with the same iterative troubleshooting approach until such scores return to an acceptable level.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional patent applicationSer. No. 63/003,725, filed Apr. 1, 2020 and entitled “VirtualMaintenance,” the disclosure of which is hereby incorporated byreference as if fully set forth herein.

BACKGROUND Field of Art

The present invention relates generally to the automated optimization ofthe health of residential and commercial infrastructure, and moreparticularly to sensor-based systems that quantify dynamic changes tothe systemic health of one or more properties, diagnose abnormalconditions (including suboptimal performance) and perform proactiveiterative troubleshooting steps that facilitate actions by owners andservice providers to address the underlying causes of such abnormalconditions efficiently over time.

Description of Related Art

The long-term maintenance of equipment and other infrastructure on thepremises of virtually any home or business is a well-known problem thathas received relatively little attention over the years. Even lessfrequently addressed is the systemic performance of a residence orneighborhood and its component equipment systems and infrastructure.

For example, higher-level concepts such as reliability and energyefficiency are routinely considered during the planning and constructionof a building. But such concepts are rarely if ever monitored andoptimized as conditions change over time after the buildings areoccupied. Moreover, while one can quantify such concepts with respect toindividual equipment, it is much more difficult to do so at a moresystemic level involving equipment systems or an entire household orneighborhood.

While much of the following discussion relates to residentialinfrastructure (such as HVAC, water control, pool and solar systems, aswell as plumbing, electrical, crawl spaces, etc.), the problems andsolutions set forth below apply equally to infrastructure and industrialequipment in commercial businesses, government entities and other small,medium and large enterprises.

Homeowners, for example, typically treat their home's infrastructure assilos of expensive items of equipment (refrigerators, freezers, ACunits, furnaces, boilers, sump pumps, dehumidifiers, pool pumps, solarpanels, etc.) that operate over relatively long periods of time andrequire only occasional repair and (only if necessary) eventualreplacement. Homeowners often are unaware of problems before conspicuoussymptoms arise, are uncertain of which provider can best address suchsymptoms, and are hesitant to incur the expense of calling servicetechnicians unless and until significant problems are evident.

Moreover, homeowners often do not maintain such equipment as frequentlyas recommended. When they do eventually call service technicians,problems are often more severe and require relatively expensive repairs.As a result of such “reactive” maintenance, the life of such equipmentis often shorter, while its cost of ownership is higher and itsoperation over time is less efficient than if such equipment had beenmaintained in a proactive and more effective manner.

It should be noted that the term “maintenance” is used herein to addressan array of different types of problems that occur with respect to aproperty's equipment and other infrastructure. For example, particularequipment may be in need of “repair” because one or more of itscomponents are broken and need to be fixed. Or “preemptive maintenance”may be recommended to avoid a future repair, thereby enhancingreliability and reducing overall costs. Or the “operational performance”of particular equipment may fail to satisfy a homeowner's desired levelof efficiency or comfort, or may simply be suboptimal, potentiallyindicating an emerging risk.

Moreover, any of these and other related types of problems may besystemic in that they are not isolated to a single piece of equipment,and may involve systems of equipment or even whole-home or multi-homeissues. The interdependence among the equipment and infrastructure of ahome only exacerbates the difficulty of troubleshooting certainproblems. Such systemic problems may require systemic solutions, such asadding, upgrading or replacing particular equipment, modifying devicesettings or otherwise altering a home's infrastructure.

As “smart homes” and the “Internet of Things” (“IoT”) have proliferated,so too has the ability to monitor device operation and detect abnormalconditions—particularly with the application of machine learning andother forms of artificial intelligence (“AI”). Yet the primary focus ofthese smart systems has been on the control and interoperability ofconnected devices, rather than on the detection of abnormal conditionsand the extensive troubleshooting expertise necessary to resolve manysuch conditions.

Some existing smart home systems monitor device characteristics thatchange over time and generate automated alerts or notifications when anabnormal condition or actionable event is detected. For example, asystem from Alert Labs (described in published US patent applications2018/0365957 and 2018/0375680) employs water, temperature, electricaland other sensors to monitor characteristics of particular equipment(HVAC systems, sump pumps, etc.) and the general environment within thehome. It further includes an analytics engine (e.g., employing machinelearning) to analyze the sensor data for the purpose of detectingabnormal conditions and issuing alerts to notify homeowners.

But, not unlike the satirical “dental monitor” television commercials inwhich a patient is diagnosed with a dental problem (e.g., a cavity) yetis sent home without the problem being fixed, these systems offer littlein the way of resolving abnormal conditions beyond providing an alert ornotification of the condition. While there is certainly significantbenefit to early detection of minor as well as urgent problems,homeowners are left to their own devices to troubleshoot and addresssuch problems. As a result, they will still likely adopt a “reactive”approach, whether they frequently call service technicians orprocrastinate due to cost concerns.

Other systems go a step beyond merely monitoring devices and providingalerts indicating the existence of abnormal conditions. For example, asystem from Google (described in U.S. Pat. No. 10,423,135) monitors theactivities of occupants as well as the operation of devices in the home.In response to detecting that certain specified conditions withinpredefined user policies are satisfied, it implements such user policiesby adjusting the configuration or operation of various devices (such aslocking or unlocking doors, turning specified lights on or off,adjusting the temperature settings of thermostats to increase energyefficiency, etc.).

While allowing for user input and goal-based device control isbeneficial, it does not address the problem of detecting abnormalconditions (as opposed to detecting conditions that merely satisfy apredefined user policy). Even more importantly, it does not address thedifficult task of iteratively troubleshooting and addressing suchabnormal conditions over time. In other words, the Google system doesnot suggest how it could detect an abnormal condition such as a faultycomponent of an HVAC system or pool pump, much less how it wouldtroubleshoot such a problem.

Still other systems, such as one from Powerhouse Dynamics (described inU.S. Pat. No. 8,649,987), have taken a narrower approach to monitoringthe performance of individual appliances (e.g., on dedicated circuitbreakers). The Powerhouse Dynamics system measures an appliance'selectrical consumption over time and compares its performance to that ofother appliances stored in the system's database (e.g., appliances ofthe same model, as well as other comparable appliances, such as newerand more energy-efficient ones).

Upon detecting a significant variance in the performance of theappliance as compared to that of other appliances in its database (suchas irregular or particularly inefficient electrical usage), the systemnot only issues an alert to notify the user, but also includes arecommendation from its “recommendation database.” Such recommendationsinclude information on cost/energy savings of other comparableappliances, as well as appliance diagnostics andmanufacturer-recommended remedial or corrective actions (such as turningan appliance or its circuit on or off).

While the inclusion of such additional recommendations is useful, thissystem still falls far short of providing actions that are tantamount tothe iterative series of steps that users and service providers wouldperform over time to troubleshoot and address underlying problems, asopposed to the symptoms corresponding to the alert. Moreover, thePowerhouse Dynamics system is limited not only to monitoring electricalconsumption, but to monitoring the performance characteristics of anindividual appliance (as opposed to taking a systemic approach todetecting and resolving abnormal conditions). In other words, it failsto obtain (and therefore cannot utilize in its recommendations) a“contextual awareness” of the home's operation over time.

As alluded to above, abnormal conditions cover a wide gamut of potentialproblems, many of which are extremely difficult to troubleshoot, evenfor a professional service provider. Only the most trivial of suchproblems is likely to be resolved by a manufacturer-supplied recommendedstep or procedure (even assuming the problem is confined to that singleappliance).

Moreover, it is inefficient for homeowners to choose between calling aservice technician whenever the slightest symptom appears (assuming thehomeowner is even aware of who to call), or to wait until severesymptoms arise. There is clearly a need for a system that performsiterative troubleshooting on a recurring basis over time, and offloads asignificant portion of this complex task from homeowners and providers.

Many complex problems require that steps be taken over time, withsubsequent steps dependent on the results of prior ones, as well as onchanges that may only be reflected by monitoring sensor-based andenvironmental data. Subsequent symptoms may differ, but still be relatedto one another (and to one or more underlying problems) over time, andmay even involve multiple appliances or other infrastructure in thehome.

In an ideal world, one would have access to a free professionalfacilities manager onsite on a 24/7 basis, who needed no sleep and had aperfect memory of all (current and historical) local sensor and externalenvironmental data, as well as performance models of all equipment andother infrastructure that are or could be installed in the home (not tomention the ability to detect suboptimal performance with respect tohigher-level concepts such as energy efficiency throughout the home).

Such an ideal “virtual facilities manager” would detect, anticipate andaddress all such problems in a systemic manner that aligns the goals ofhomeowners and various service and other providers, and thus reveals themany shortcomings in these existing systems, including additional costdue to a reactive maintenance approach, lower efficiency of equipmentoperation over time and an inability to detect and resolve systemicproblems affecting multiple items of equipment and infrastructure, amongothers.

In short, there remains a need for a system that not only can monitor(on a continuous basis) sensor-based and external environmental datarelating to the operation of equipment and other infrastructure in thehome, and detect abnormal conditions over time (from a systemic as wellas individual device perspective), but also can address such abnormalconditions by issuing contextual alerts and simulating the iterativetroubleshooting steps (with the integral involvement of homeowners andproviders) that a theoretical virtual facilities manager would performover time.

Such a system would, in effect, go well beyond a “check engine light forthe home,” in that it would eliminate the need for many expensiveservice calls and, when certain service calls were necessary, wouldprovide service providers with a valuable “head start” in thetroubleshooting process. Service providers would also benefit not onlyfrom the leads they can obtain via such an automated system, and fromthe ability to handle a greater quantity of less complex service calls,but also from the interactive relationships they can develop andmaintain with customers via such an integrated system.

Moreover, such a system would optimize the health of the home (inaccordance with an owner's predefined goals) by continually monitoringand quantifying higher-level systemic health indicators (such asreliability, energy efficiency and maintenance costs), issuing alertsupon detecting suboptimal performance and recommending and facilitatingthe implementation of iterative troubleshooting steps to address suchsuboptimal performance.

SUMMARY

The present invention addresses the shortcomings of existing systemsdescribed above with a novel “Virtual Facilities Manager” architecturethat combines multiple prediction engines, fed by continuously monitoredand processed sensor-based and environmental data (historical as well ascurrent), with integrated networks of homeowners and service and otherproviders, such as manufacturers, retailers, installers, insurers, homewarranty companies and even institutional home-maintenance providers.The integration of these various components into a Virtual FacilitiesManager (“VFM”) system enables a continuous feedback process in whichabnormal conditions are detected and addressed by issuing contextualalerts with associated actions in an iterative manner over time,recognizing that many underlying problems can only be addressedeffectively via a complex diagnostic and iterative troubleshootingprocess requiring multiple related actions over time (“relatedalert-action pairs”).

At a conceptual level, the VFM system optimizes “home health” byemploying a proactive and iterative troubleshooting process that detectsand addresses a variety of underlying problems as they arise over time.Certain problems involve a single item of equipment, while others aresystemic in nature. Whether systemic or otherwise, some problems reflectbroken components in need of repair, while others reflect preemptivemaintenance issues designed to maintain “healthy” and reliable operationto avoid future repairs, and still others reflect suboptimal operationof the home and its component systems over time (such as a gradualdecrease in systemic energy efficiency, not easily remedied, forexample, by the replacement or upgrade of one item of equipment).

Contextual alerts take into account the interdependencies among systemsof equipment and home infrastructure, as well as external environmentalfactors. As a result, the VFM system anticipates prospective problemsand acts accordingly. In this regard, the VFM system anticipatescorresponding systemic solutions—in particular, the next action mostlikely to resolve the underlying problem of which the current alert isbut a symptom. Over time, the solution (a set of related alert-actionpairs) may well involve multiple pieces of equipment or otherinfrastructure throughout the home.

Moreover, by integrating homeowner and provider networks (i.e., into a“Homecare Network”), the VFM system expands its iterativetroubleshooting approach to align the goals of homeowners and providers.For example, the VFM system generates actions that are optimized tosatisfy specified “User Goals” relating to cost, energy efficiency,reliability and a host of other related factors (alone or incombination) as described below. Such actions also take into account thecapabilities of particular occupants—e.g., to perform particular tasksthemselves rather than requiring help from an external provider.

In one embodiment, the VFM system also takes into account “ProviderGoals” and capabilities. For example, the VFM system may recommend aservice provider with specific expertise matching the suspected problemdiagnosed via a series of related alert-action pairs. Beyond serviceproviders, the VFM system also assesses the goals as well as thecapabilities of other providers (e.g., insurers and home warrantycompanies) to align such goals with those of particular homeowners (asdiscussed below).

As will become apparent from the following discussion, the VFM systemprovides significant advantages to both homeowners and providers. Whileearly-warning alerts are useful to providers as well as to homeowners,they are in the first instance most useful to the VFM system itself,which logs alerts (that may or may not exceed a threshold for informinghomeowners or providers) as well as actions in a “Home Health Record”for subsequent use by the VFM system in generating future alerts andcorresponding actions.

Certain alerts are handled entirely by the VFM system itself, whether ornot the homeowner is notified. For example, the VFM system may reset or“cycle” a device as an initial troubleshooting step to address theabnormal condition that gave rise to the alert. In a subsequentiteration (e.g., after an appropriate period of time has elapsed), thesystem may or may not generate that same alert, depending upon theextent to which this first troubleshooting step was effective.

In other situations, the VFM system may ask the homeowner to performthat step. For example, one troubleshooting step may include a requestto cycle a circuit breaker or check a particular setting or other stateof certain equipment. That step may also include a request for feedback.But, in many situations, that first step and any feedback will merely be“input” to subsequent iterations of the VFM system that may or may noteventually result in the same or a related alert.

In this manner, the VFM system generates an iterative sequence oftroubleshooting actions over time in an effort to determine theunderlying cause of abnormal conditions that gave rise to a set ofrelated alerts and corresponding actions. It is important to emphasizethe distinction between this dynamic set of related alert-action pairs(generated iteratively via multiple integrated prediction engines) and astatic predetermined manufacturer-supplied procedure (or even apredicted multi-step process).

In one embodiment of the present invention, every subsequent relatedalert-action pair is dependent upon the one that preceded it, as well asupon the interim changes in the sensor and environmental data. Just asan algorithmic music service only generates a single “next” song at atime, the VFM system typically generates only the “next action” to beperformed—because subsequent actions or troubleshooting steps aredependent upon future conditions that typically cannot be predeterminedas a practical matter.

As noted above, certain steps are performed entirely by the VFM system,while others are performed by homeowners and may eventually require oneor more service calls. Because many problems cannot be addressedadequately without the benefit of time between troubleshooting steps,this process minimizes the need for service calls and makes such servicecalls, when necessary, far more efficient.

Moreover, the integration of the Homecare Network into the VFM systemenables various providers to participate in the troubleshooting process(i.e., “human troubleshooting”) long before service calls may becomenecessary. In many cases, such participation may delay or even preventservice calls that might otherwise have been inevitable. When a servicecall is required, the technician benefits from prior troubleshootingefforts, even those from other providers that may not even be in thesame geographic area.

Finally, it should be reiterated that the VFM system applies the sameiterative troubleshooting approach to the “problem” of suboptimalperformance. Even if all equipment is operating as expected, the VFMsystem may detect that the home's “Energy Efficiency” score has fallenbelow a predefined threshold. Resolving this underlying problem mayrequire a series of different recommended actions implementediteratively over time until the score returns to an acceptable level(e.g., recalibrating one or more thermostats, replacing an older lessefficient water heater and adding solar panels).

The following description will illustrate the architecture and keycomponents of the present invention, as well as the dynamic processesthat the VFM system performs over time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram illustrating an embodiment of the keycomponents of the present invention.

FIG. 2 is a flowchart illustrating an embodiment of the key steps of adynamic process by which the present invention maintains the health ofthe equipment and infrastructure of one or more properties.

FIG. 3A is an architectural diagram illustrating an embodiment of aneural network implementation of a scoring engine of the presentinvention, including key inputs and outputs.

FIG. 3B is an architectural diagram illustrating an embodiment of aneural network implementation of an alert generation engine of thepresent invention, including key inputs and outputs.

FIG. 3C is an architectural diagram illustrating an embodiment of aneural network implementation of an action and goals optimization engineof the present invention, including key inputs and outputs.

DETAILED DESCRIPTION

Turning to FIG. 1, the primary functionality of the VFM system 100 isembodied in a VFM Server 110 that communicates over the Internet 105with integrated networks of (i) providers, including Service Providers130 and Other Providers 140 (eg, equipment vendors, insurance andwarranty providers, etc.) and (ii) users (including homeowners, businessowners and other users) from their desktop, laptop and mobile devices160 and from their Home and Business Premises 150. The VFM system 100also integrates with External Data Sources 128, for example, to obtainenvironmental data, such as weather data, as well as air, water and soilquality data and event data (eg, regarding recent earthquakes, floods ordisease outbreaks).

VFM Server 110 can be implemented as a single physical server device, asillustrated in FIG. 1 (including standard hardware, firmware andoperating system 111), or as multiple interconnected servers (e.g., viaInternet 105). Such server functionality can also be implemented, inother embodiments, across one or more physical devices, and bydistributing the functionality of modules illustrated in FIG. 1 into afewer or greater number of modules (including different allocations ofhardware and software functionality).

VFM Server 110 further includes Data Monitor 115 (explained in greaterdetail below) to collect and process data from premises (e.g., sensordata), providers and other external sources, such as External DataSources 128. Such data (both current and historical) are fed intoprediction engines to detect abnormal conditions, generate alerts andcorresponding actions on an iterative basis over time.

VFM Server 110 employs Prediction Engine Manager 117 to manage a set ofprediction engines to generate alerts and corresponding actions, asdescribed in greater detail below. In one embodiment, these predictionengines include Scoring Engine 118 (to manage scores and subscoresrepresenting systemic states of an individual premises, as well ascomponent systems, equipment and infrastructure), Alert GenerationEngine 120 (to generate alerts reflecting the occurrence of abnormalconditions) and Action and Goals Optimization Engine 122 (to generateactions corresponding to such alerts in a manner that optimizesuser-specified goals).

Once VFM Server 110 generates an alert and corresponding action, aCommunications Assistant (i.e., a “Home Assistant”) 124 manages theprocess of communicating alert-action pairs (and related metadata andother summarized historical data from Home Health Record 126—in oneembodiment stored in Database 25) to homeowners and/or providers, asdiscussed below. For example, in one embodiment, CommunicationsAssistant 124 follows up with homeowners to determine whether particularactions were implemented (e.g., changing an air filter). Suchinformation is then updated in the homeowner's Home Health Record 126,and may even be utilized, for example, by providers (service providers,insurance providers, warranty providers, etc.) to offer “credits” forsuch home maintenance tasks.

While Communications Assistant 124 manages communications to and fromVFM Server 110, Check Engine Light Module 123 includes a set of APIs(and corresponding SDKs) that facilitate the creation of third-partyapplications (such as smartphone apps, or applications on desktop andlaptop computers) which integrate and extend the functionality of VFMsystem 100.

Premises and User Manager 114 manages the common and uniquecharacteristics of various homeowners (and owners of businesses andother types of enterprises) across the Homecare Network. This includesmanaging the process of acquiring information about the various premisesand their owners across the Homecare Network, and leveraging suchinformation to create and manage specific tasks unique to particularpremises and/or their owners (as well as other users) via their desktopor laptop computers or mobile devices 160.

In addition to interacting with homeowners, Communications Assistant 124(with the assistance of various “provider manager” modules) alsofacilitates interactions with providers in accordance with preconfiguredprotocols. For example, when a service call is deemed necessary,Communications Assistant 124 and Service Provider Manager 112 (alongwith Other Providers Manager 113) together manage the process ofautomatically providing relevant summary data (current alert-actionpair, relevant service history, etc.) to specified service providers viathe Homecare Network. In one embodiment, service providers are involvedeven before a service call is deemed necessary, enabling additionaltroubleshooting input by service providers, which reduces the likelihoodof an eventual service call.

As a result, homeowners reduce their out-of-pocket expenses for servicecalls over time, as well as benefit from increased efficiency andreliability due not only to the early detection of abnormal conditions,but to the corresponding troubleshooting actions designed to addressthose conditions over time. In short, homeowners experience greaterenergy efficiency, reliability, safety and extended life of theirequipment and other infrastructure.

Moreover, from an operational standpoint, VFM system 100 reduces theoverall cost and operational efficiency of the home infrastructure on asystemic basis. For example, VFM Server 110 generates alerts that arenot necessarily restricted to an individual item of equipment, but mayrelate to the performance of a system of equipment (e.g., an HVACsystem) that includes multiple items of equipment and infrastructure(e.g., an AC unit, condensate pump, furnace or boiler, ducts, dampers,thermostats, etc.). Corresponding actions may include troubleshootingsteps with respect to multiple pieces of equipment and otherinfrastructure (and components thereof) within that system (or in somecases even outside of a particular system).

Service providers also benefit from this VFM architecture in that theybecome part of the integrated Homecare Network. For example, serviceproviders in a homeowner's geographic area receive automated leads tomany homeowners in that area. By leveraging the opportunity toparticipate in the troubleshooting process (e.g., receiving detailedrelevant data and providing troubleshooting input even before a servicecall is required), service providers can increase their chances ofturning such leads into actual business (as well as be incentivized inother ways to encourage their participation from geographically distantareas).

Moreover, their cost of each “truck roll” can be reduced significantlyby virtue of receiving detailed relevant service history in advance of aservice call. Many service calls can be avoided, and those that arenecessary are more efficient as a result of the service provider'scontextual awareness of the recent and historical performance of systemsof equipment (even beyond a “suspect” individual item of equipment).

The Homecare Network of VFM system 100 also integrates other types ofproviders beyond service providers, and aligns their goals with those ofparticular homeowners. For example, networks of manufacturers, retailersand installers are integrated in a manner that enables the need for newequipment to be interpreted (with the homeowner's permission) as anopportunity to market, sell and install a particular model of equipment.

Such need may be evident, for example, from a recommended alert-actionpair, or simply by virtue of the age of the equipment or its performancecharacteristics over time, alone or in conjunction with a larger systemwithin the home. Homeowners may reach out directly to such integratedproviders via the Homecare Network. And the providers may find thattheir marketing efforts are far more targeted, and thus more effective,in light of their detailed knowledge of the home infrastructure andequipment performance.

An integrated network of warranty providers may, for example, enable awarranty holder (with respect to one or more items of a homeowner'sexistent equipment) to recommend particular actions for the purpose ofavoiding a future warranty claim. Such a company might also offer torenegotiate terms or offer discounts on an extended warranty (e.g.,based upon knowledge of a homeowner's diligent maintenance practices andhistorical operational performance).

Other warranty companies not currently affiliated with the homeowner may(based on current and historical performance data regarding thehomeowner's equipment and infrastructure) market new or replacementwarranties to the homeowner. In this manner, both the warranty companiesand the homeowner benefit from a competitive market that is far moretargeted to homeowners “in need” but not necessarily “in distress.”

Similarly, VFM system 100 integrates a network of insurance companies(with or without an existing relationship to the homeowner) that canleverage this targeted knowledge about homeowners' equipment andinfrastructure to offer competitive policy rates (including group ratesacross other homes within the network). Homeowners can also leveragetheir “preemptive maintenance” history to shop around for morecompetitive rates directly from insurance companies with an intimateknowledge of certain risks associated with their home's infrastructure.

One particularly valuable provider network includes multi-residenceinstitutional owners who desire to reduce the costs of maintaining theequipment and infrastructure across their various properties. Instead ofrelying on manually managing multiple property managers across variousgeographic locations, such institutional providers can instead leveragethe “networked property management” features of VFM system 100 via theHomecare Network.

Moreover, these institutional owners can leverage their “volume”purchasing and maintenance power with respect to equipment andinfrastructure across such properties to offer their homeownerssignificant savings and convenience. Not only will homeowners benefit,but service (and other) providers will also benefit from access to theselarge multi-residence markets.

Such efforts may ultimately move the market further in the direction ofan “Equipment-as-a-Service” (“EaaS”) model, in which homeowners leasesome or all of their equipment and infrastructure for a monthly(possibly consumption-based) fee that includes service, upgrades andreplacement costs, as well as valuable premium discounts on certainitems. Under this model, homeowners enjoy the benefits of regular “homemaintenance” payments, while avoiding concerns about expensiveunexpected and intermittent repair and replacement costs. Yetinstitutional owners will still have incentives to proactively maintainthe equipment and infrastructure across their properties, as suchproactive maintenance will reduce their costs over time.

It will be apparent to those skilled in the art that a variety of otherintegrated provider networks will provide similar benefits (as comparedto the Homecare Network) to homeowners and providers alike. In eachinstance, the integrated providers benefit from access to a verytargeted market of homeowners based on the detailed current andhistorical service and related data provided by VFM system 100.Homeowners similarly benefit from the reduced costs afforded by a“volume purchase” environment, in addition to the convenience and accessto targeted experts that are “up to speed” with the state of their homeequipment and infrastructure. In short, the Homecare Network of VFMsystem 100 provides an integrated mechanism to align the goals ofhomeowners with those of various provider networks.

In one embodiment, Home Health Record 126 includes static as well asdynamic information. For example, it contains the home's geographiclocation and identification of installed equipment and infrastructure,including its location (e.g., room or more precise position) within thehome. It also includes user profile information, such as an occupant'sability and/or desire to perform certain troubleshooting tasksthemselves.

The Home Health Record 126 further includes operational models,performance characteristics and specifications (in certain casesspecific to the home's geographic location and environment) for eachinstalled item of equipment, as well as for comparable units, such asthose that are newer and more energy-efficient and others that may besmaller or larger alternatives from a systemic perspective. In oneembodiment, Home Health Record 126 includes information regarding homeinfrastructure that is not specific to any item of equipment, such asthe thermal rating of windows.

Dynamic information includes the age of each item of equipment, warrantyinformation and service history (including identification of serviceproviders). Moreover, the dynamic history of the home includes metadatarelating to prior alerts and actions, as well as timestamped raw and/orprocessed sensor and external environmental data.

In one embodiment (discussed below), Home Health Record 126 alsoincludes scores reflecting the systemic state of the home itself (aswell as sub-scores reflecting equipment systems and individual units ordevices). For example, a Reliability score might reflect an overallcurrent state of the reliability of the home (taking into account thereliability over time of individual pieces of equipment). In thisembodiment, trendlines of these scores (and sub-scores) are alsoincluded in Home Health Record 126.

Home Health Record Generator 116 creates and maintains Home HealthRecord 126 as certain information dynamically changes over time. Forexample, whenever a new homeowner is added to VFM system 100, HomeHealth Record Generator 116 creates entries for the various staticinformation relating to that homeowner's residence (e.g., user profiledata, geographic data, equipment and infrastructure and relatedoperational data, etc.). Moreover, as dynamic sensor data is receivedand processed over time, Home Health Record Generator 116 stores andupdates Home Health Record 126 to reflect such dynamic data, includingalerts, actions and other related data for access by users, providersand various components of VFM system 100.

In addition to integrating VFM Server 110 with Service Provider 130 andOther Provider 140 networks, the Homecare Network also integrates thepremises 150 of homeowners and owners of business and other entities. Inthis manner, VFM system 100 monitors the changes that occur over time tothe infrastructure and equipment within those premises 150, detectsabnormal conditions and generates related alert-action pairs in aneffort to address those abnormal conditions over time (e.g., iterativelytroubleshooting the root cause of underlying problems and providingproactive longer-term home maintenance solutions).

At a typical homeowner's premises 150, VFM system 100 includes a SensorNetwork 156 to monitor the various equipment and infrastructureinstalled in the home, such as Devices 154 in various locationsthroughout premises 150. It should be emphasized that individual sensorsdo not necessarily bear a one-to-one relationship with each item ofequipment. For example, a leak detection sensor may be placedstrategically at or near a pipe valve interconnecting multiple pieces ofequipment. Moreover, temperature sensors may be placed in a particulararea to ensure that excessive heat (or cold) does not negatively impactthe operation of nearby equipment. While the permutations are infinite,it is important to note that Sensor Network 156 provides continuous datathat may only indirectly relate to particular equipment.

Moreover, the placement of such sensors is of particular importance. Inone embodiment, information about the precise location of each sensor(relative to nearby equipment and its room and home environment) isstored in Home Health Record 126 and utilized in the process ofpredicting alerts and corresponding actions. A potential action may alsoinclude the addition, removal or relocation of one or more sensors.

In one embodiment, an on-site Premises Controller 158 filters andprocesses data from Sensor Network 156 before delivering it over theInternet 105 to VFM Server 110. Premises Controller 158 filters suchdata in part to address the impracticality of sending to VFM Server 110the entirety of the raw data generated by every sensor.

Sensors may well vary in how frequently they generate raw data and howfrequently they are sampled. Premises Controller 158 effectively“normalizes” this variability across different sensors through thefiltering process, in which data for certain types of sensors are moresignificantly filtered than for other sensors (depending, for example,on the likely rate of change of such raw data over time).

Moreover, Premises Controller 158 also normalizes the units of raw dataamong various sensors through a conversion process. As a result, theconverted data sent by Premises Controller 158 to VFM Server 110 can bemeaningfully compared across different sensors (e.g. to determinerelative statistically significant changes over time).

In one embodiment, the frequency and granularity of monitored data (aswell as the schedule for delivery of such data to VFM Server 110) isadjustable based upon actions (i.e., commands) from VFM Server 110. Forexample, to diagnose a potential abnormal event, VFM Server 110 mayinvoke Premises Controller 158 to automatically adjust the frequencyand/or granularity of monitored data to gather more information tofacilitate the prediction of further alerts and/or correspondingactions.

Communication with VFM Server 110 is managed by VFM Server InterfaceModule 152, which communicates with Communications Assistant 124 on VFMServer 110 via Internet 105. Premises Controller 150 and VFM ServerInterface Module 152 may be implemented on a standard desktop or laptopcomputer (along with standard hardware, firmware and operating system151) or, in other embodiments, on a smartphone or as separate physicalstandard or custom hardware devices.

Homeowners (and owners of businesses and other types of premises) canalso access VFM Server 110 via a voice-enabled and/or web browserinterface or custom app 165 on their devices 160, such as mobiledevices, or through similar interfaces via their laptop or desktopcomputers (each of which typically includes standard hardware, firmwareand operating system 161).

In this manner, homeowners receive alerts and corresponding actions, aswell as provide feedback (e.g., requested information) or initiatequeries. For example, a homeowner can submit a voice query regarding therelative efficiency of their AC system within a specified timeframe, andreceive a summary of historic performance levels, as well as targetedcomparative recommendations for upgrades or replacements that will bemore efficient from the perspective of their AC system, as well as anyindividual item of equipment.

Another key component of the architecture of VFM system 100 (alluded toabove) is the Homecare Network that integrates the homeowner networkwith networks of various service and other providers. Each ServiceProvider system 130 includes (in addition to standard hardware, firmwareand operating system 131) a VFM Server Interface Module 132 (e.g.,embodied in the provider's physical server, whether on-premises or inthe cloud) that interacts with corresponding Service Provider Manager112 functionality in VFM Server 110. In other embodiments, ServiceProvider system 130 may be implemented as one or more physical desktopor laptop servers (and/or mobile or customized hardware or softwaredevices).

Similarly, Other Provider systems 140 (e.g., owned by equipment vendors,insurance or warranty companies, etc.) also include standard hardware,firmware and operating system 141, along with a VFM Server InterfaceModule 142.

The VFM Server Interface Module 132 or 142 in each provider's servercommunicates with the Service Provider Manager 112 or Other ProviderManager 113 in VFM Server 110. It receives and analyzes data from VFMServer 110 (such as periodic data derived from Home Health Record 126summarizing the state of each homeowner's equipment and infrastructure)and provides feedback, such as recommended actions regarding a recentalert, discount service and upgrade offers, etc. Such data can beanalogized to a “carfax” report with respect to the state of ahomeowner's equipment and infrastructure over time.

In addition to the advantages of repeat business and upsellopportunities, this architecture enables benefits such as “just-in-time”service calls (e.g., based on knowledge of the state of a homeowner'sequipment) and better overall matching of specific needs of homeownerswith providers having relevant expertise. A service provider might, forexample, combine a just-in-time service call with an upcomingmaintenance task. In one embodiment, custom APIs facilitate moreefficient and complex functionality, such as detailed “just-in-time”scheduling, troubleshooting actions with immediate feedback to theprovider, etc.

In other embodiments, VFM Server 110 matches the “supply” of providerswith the “demand” of a network of properties (including institutionalowners of multiple properties). For example, VFM Server 110 can leverageits knowledge of the availability of a particular service provider'sschedule and the need for relevant service by multiple homeowners withina particular geographic area (each of whom has received an outstandingaction calling for such service within a month). As a result, bothhomeowners and providers benefit from the enhanced efficiency affordedby this “integrated scheduling” functionality.

As noted above, VFM system 100 functional modules depicted in FIG. 1 canbe implemented in hardware, software and combinations thereof, and suchfunctionality can be divided into sub-modules or combined togetheracross one or more physical servers in accordance with design andengineering tradeoffs by those skilled in the art without departing fromthe spirit of the present invention.

Moreover, to the extent that functional modules within VFM Server 110(or those included in Premises Controller 158 or in provider or otherexternal servers) are implemented in software, such software is embodiedin physical non-transitory computer-accessible storage media (i.e.,memory) from which it is invoked for execution by one or more CPUs orother physical processing units.

As noted above, Premises Controller 158 continuously processes raw datafrom the sensors via local sensor network 156 at the premises 150. Afterfiltering and converting this raw data as described above, PremisesController 158 streams this processed sensor data to Data Monitor 115 inVFM Server 110.

Data Monitor 115 also receives environmental and other external datafrom a variety of external data sources 128. For example, local weatherforecasts provide valuable input that affect predictions of abnormalconditions. Current heavy rain might explain why elevated sump pumpusage is not abnormal, while a storm warning might result in preventivecharging of batteries in a solar system. Other external environmentaldata can include air, water and soil quality, as well as major nearbyevents, such as an earthquake, flood or infectious disease outbreak orpandemic.

Data Monitor 115 parses this sensor-based and environmental data foreach home, and processes and formats the data for input to theprediction engines. It should be noted that the processed data providedto the prediction engines includes timestamped current and historicalraw data (in one embodiment), as well as data that have been filteredand converted by Premises Controller 158.

Such information enables the prediction engines to infer proactivelyvarious states of the equipment, systems and infrastructure of the home.For example, is a unit of equipment (or a component thereof) wearingmore rapidly than normal, or nearing the end of its useful life? Is aunit of equipment or a system (e.g., an HVAC system) operating more orless efficiently than normal? These and other current or prospectiveoperational states can be inferred from this raw or processed data.

In addition to this processed current and historical sensor andenvironmental data, additional information from Home Health Record 126is extracted for use by the prediction engines. For example, historicalalert and corresponding action data is extracted as well, includingmetadata from such alerts and actions, such as timestamps, types orcategories and various other attributes (discussed in greater detailbelow).

Such inputs further include the home's geographic location andidentification of installed equipment and infrastructure, including itslocation (e.g., room or more precise position) within the home, as wellas its age, warranty information and service history (including serviceprovider info). Also included are operational models, performancecharacteristics and specifications of installed equipment and comparableunits, as well as user profile information (e.g., reflecting particularoccupants' desire and ability to perform certain levels oftroubleshooting). In one embodiment, data across multiple properties areprovided as input to enable the prediction engines to consider“inter-property” factors such as shared risks and pooled resources,comparative performance benchmarks, etc.

It will be apparent to those skilled in the art that a variety of othertypes of information (whether from sensors, external data sources orelsewhere) can be included in Home Health Record 126 and provided asinputs to the prediction engines without departing from the spirit ofthe present invention. Once Data Monitor 158 has completed the parsing,formatting and other processing of this data, Home Health Record 126 isupdated with this newly processed data, which are provided to theprediction engines.

Before discussing the prediction engines, it is helpful to consider anoverview of the dynamic aspects of the iterative process by which thekey components of VFM system 100 work together to process raw data overtime, feed such data (both current and historical) into predictionengines to detect abnormal conditions, and generate alerts andcorresponding actions designed to address the root cause of underlyingproblems of which such abnormal conditions are often mere symptoms.

Turning to FIG. 2, flowchart 200 illustrates one embodiment of such acontinuous process. While flowchart 200 focuses on the process within aparticular premises 150, it should be noted that VFM Server 110 managesmultiple different premises 150 simultaneously. In certain scenarios,this enables cross-premise synergy, such as a provider coordinatingmultiple service calls based on similar alerts or other problems acrosspremises 150 within its service area.

Beginning with step 201, the sensors in sensor network 156 capture rawsensor data as air, water, soil and other sensors detect changes overtime. Premises Controller 158 filters, converts and otherwise processessuch data (as discussed above) and, in step 205, transmits suchprocessed sensor data to Data Monitor 115 in VFM Server 110.

In step 210, Data Monitor 115 further processes such sensor data, alongwith historical data from Home Health Record 126 and environmental data(e.g., from External Data Sources 128), and parses such data into aformat suitable for input to the prediction engines. In step 215, DataMonitor 115, in conjunction with Home Health Record Generator 116,updates Home Health Record 126 to reflect the new data since the prioriteration of this process 200.

In step 216, Data Monitor 115, in conjunction with Prediction EngineManager 117, provides such inputs to Scoring Engine 118 (thefunctionality of which is described in greater detail below), whichupdates relevant scores (home reliability, home efficiency, fire risk,etc.). Though not shown, Home Health Record 126 is updated to reflectthese revised scores.

In one embodiment, Prediction Engine Manager 117, in step 216, providesthe same inputs, along with these updated scores, to Alert GenerationEngine 120, which, if it detects an abnormal condition, generates (instep 220) an alert with associated attributes, as is also discussed ingreater detail below. In step 225, Prediction Engine Manager 117determines whether the predicted alert should be issued as an alertrequiring an associated action. If not, Home Health Record 126 isupdated in step 290, and the process returns to step 201 to process rawsensor data during the next iteration of process 200.

Otherwise, the alert is processed by Prediction Engine Manager 117,which (in one embodiment shown in step 228) provides the predicted alertand associated attributes, along with the scores and other input datafrom Home Health Record 126, to Action and Goals Optimization Engine122. In step 230, Action and Goals Optimization Engine 122 generates anaction (i.e., the next recommended troubleshooting step, as discussed ingreater detail below) that corresponds to the current alert (thuscreating a current alert-action pair) and is optimized in accordancewith a homeowner's predefined user goals.

In step 250, Prediction Engine Manager 117 processes the currentalert-action pair (e.g., to implement that next recommendedtroubleshooting step) which, in one embodiment, involves coordinationwith Communications Assistant 124 and Premises and User Manager 114(among other modules of VFM Server 110) to determine, in step 252, theinteraction with and communication to the relevant premises 150 andassociated users 160. For example, a recommended action may bedetermined to require communication to a homeowner to implement thataction. In another embodiment, such action may be performedautomatically by Premises Controller 158.

Similarly, in step 254, Prediction Engine Manager 117 coordinates withCommunications Assistant 124 and Service Provider Manager 112 and OtherProviders Manager 113 (among other modules of VFM Server 110) todetermine the interaction and communication with any relevant ServiceProvider 130 or Other Provider 140. For example, a recommended actionmay be performed entirely or in part by a provider (e.g., scheduling aservice call). In another embodiment, a relevant provider may supplementthe recommended action with additional feedback.

Finally, in step 260, the recommend action is coordinated, communicatedto and implemented by the relevant parties, such as providers, users andautomated functionality built into Premises Controller 158. At thatpoint, Home Health Record 126 is updated in step 290, and the processreturns to step 201 to process raw sensor data during the next iterationof process 200. In one embodiment, the results of such implementationare incorporated into the update of Home Health Record 126. For example,a homeowner might provide feedback indicating whether or not the actionresolved the current alert (i.e., symptom of an underlying problem).

Turning to FIGS. 3A, 3B and 3C, the functionality of one embodiment ofthe prediction engines is now described. VFM Server 110 integrates threedistinct prediction engines (Scoring Engine 118, Alert Generation Engine120 and Action and Goals Optimization Engine 122), each of which isimplemented as a machine-learning-based neural network that employspredictive techniques to assess likely outcomes. In other words, given alarge set of inputs, the combination of which has very likely not beenencountered before, each of these prediction engines assesses and“predicts” the most probable outcomes.

In alternative embodiments, the prediction engines are implemented withdifferent forms of unsupervised machine learning, statistical analytics,rules-based heuristics and other techniques (or combinations thereof).In such embodiments, the prediction engines still generate similaroutputs in response to similar inputs (as compared with theirmachine-learning neural network counterparts) without departing from thespirit of the present invention.

In one embodiment, the inputs described above are provided to theprediction engines on an iterative basis. In other words, on a veryfrequent basis (e.g., once per second), the inputs are provided to theprediction engines, which ultimately may yield a predicted alert andoptimized action. During most iterations, insufficient changes haveoccurred to generate an alert. Once an alert is generated, acorresponding action (optimized for predefined User Goals) is generatedand then implemented. One embodiment of these prediction engines isdescribed below.

Illustrated in diagram 300 a of FIG. 3A, Scoring Engine 350 a utilizes aset of inputs 310 a (discussed above) to generate a set of outputs 320 arepresenting the most probable assessment of the state of certainhigher-level (e.g., whole-home) conditions and risks. For example, inone embodiment, it generates scores 325 a for overall “Home Efficiency,”“Home Reliability,” “Safety,” “Maintenance” (e.g., how well a home hasbeen maintained over time), “Fire Risk,” “Flood Risk” and “Air Quality.”All of these scores 325 a facilitate the generation of alerts andactions by the other prediction engines.

For example, a relatively low Home Efficiency score may tip the balancein generating an alert with respect to otherwise borderline efficiencydata regarding particular equipment or systems. Similarly, the predictedaction corresponding to that alert might be one that would not have beengenerated had the Home Efficiency score been higher (such as aparticular adjustment of settings, an upgrade to a more efficient modelor even the addition or replacement of particular equipment ormodification of the home's infrastructure over time until the HomeEfficiency score increases sufficiently).

In one embodiment, Scoring Engine 350 a generates sub-scores (e.g., forthe reliability of an individual item of equipment) in the process ofgenerating a score 325 a reflecting the state of the home. Thesesub-scores are also maintained in Home Health Record 126.

Scoring Engine 350 a generates outputs 325 a that reflect system-wideand, in particular, whole-home perspectives based on various aspects ofcurrent and historical sensor data regarding one or more items ofequipment and infrastructure, as well as external environmental data.For example, the reliability of an individual item of equipment may beimpacted by its age, prior alerts and corrective actions, repairs, etc.Yet, Scoring Engine 350 a will also take into account the reliability ofother equipment over time.

Based upon its training, Scoring Engine 350 a may effectively prioritizeone item of equipment over another (e.g., by assigning different weightsto different equipment types), or prioritize based on a system'soperational reliability curve or various other factors. Such weightingor prioritization is implemented as an integral part of the ScoringEngine's 350 a machine-learning architecture, as opposed to employing asimple weighted average or other predetermined function.

In one embodiment, Scoring Engine 350 a is trained with sample sets ofinputs yielding a known resulting output score 325 a (such as a FireRisk score, for example, of 85 on a scale of 1 to 100). These sampleinputs include current processed sensor data 312 a (described above),current processed environmental data 314 a (such as weather data fromExternal Data Sources 128), and various data 318 a stored in Home HealthRecord 126 (including, in one embodiment, historical sensor andenvironmental data, historical issued alerts and corresponding actions,a list of equipment with its room location, operational models andcurrent age, and profile data relating to users and providers).

As a result of such training, Scoring Engine 350 a becomes sufficientlyproficient at predicting changes in output scores 325 a when presentedwith current and historical values of this set of inputs 310 a that ithas never encountered before. Upon each iteration of VFM system 100,Scoring Engine 350 a predicts changes (if any) in these output scores325 a based upon changes in the values of the inputs 310 a that haveoccurred since the prior iteration.

Turning to FIG. 3B, diagram 300 b illustrates how, in one embodiment,Alert Generation Engine 350 b utilizes a set of inputs 310 b (in thisembodiment, the same set of inputs provided to Scoring Engine 350 a,along with the scores generated by Scoring Engine 350 a, to generate aset of outputs 320 b reflecting one or more alerts 322 b and associatedattributes 324 b. Inputs 310 b include current processed sensor data 312b, current processed environmental data 314 b, historical data 318 bfrom Home Health Record 126 (as described above) and the output scores316 b generated by Scoring Engine 350 a.

Alert Generation Engine 350 b generates predictions regarding the likelyexistence of emerging system risks—i.e., abnormal conditions. Suchabnormal conditions include “scoring anomalies” in which a particularsub-score or score (e.g., a significantly low Home Efficiency score), inthe context of the other inputs, suggests a need for corrective action.As will be discussed below, such corrective action could be adjustmentof the settings of particular equipment or, in more severe cases,addition of certain equipment or replacement of a particular unit with anewer more efficient model.

Other abnormal conditions include “environmental anomalies.” in whichenvironmental data suggest the need for preventive actions. For example,in the event of a severe storm warning within proximity to a homeowner'spremises, Alert Generation Engine 350 b might generate (taking intoaccount the context of the other inputs 310 b) a preventive action suchas testing the operation of a sump pump.

A primary function of Alert Generation Engine 350 b is to generatealerts 322 b representing “equipment anomalies” that relate toparticular equipment or components thereof (or more abstract systemicanomalies at a system or whole-home level). For example, did aparticular item of equipment (or component, or system comprisingmultiple items of equipment) experience a “malfunction?” Or is it“unreliable” or “running inefficiently” over a sufficient period oftime? Is it “likely to break” based upon its past performancecharacteristics, or “nearing the end of its life?” Or is it currently“in need of maintenance?”

These and other types of alerts 322 b represent outputs 320 b of AlertGeneration Engine 350 b that constitute abnormal conditions reflecting“equipment anomalies.” As noted above, during most iterations of AlertGeneration Engine 350 b, no alert will be generated. In other words, allsystems and individual units of equipment within the home (or evenacross a network of homes) are operating within “normal operationalranges” and “acceptable risk levels”—at least based upon current sensordata.

But, at some point in time (e.g., when sensor data or environmental datachanges sufficiently from the prior iteration), Alert Generation Engine350 b will, during the current iteration, detect an abnormal conditionand issue an alert 322 b. In addition to generating the alert itself,Alert Generation Engine 350 b also generates certain related “alertattributes” 324 b.

For example, in one embodiment, the alert attributes 324 b identify theparticular item of equipment (or component thereof, or higher-levelsystem) to which the alert 322 b applies. They further include various“quantified details” relating to such equipment and components, such asa particular temperature or other setting that may relate to theanomaly.

Finally, the alert attributes 324 b include a “severity level”indicating the relative significance of the alert 322 b. As many alertsare effectively contextual alerts, the severity level often reflects acontextual awareness of the state of the home or equipment system(beyond the state of an individual piece of equipment or componentthereof). For example, a problem with an air conditioning unit mightincrease in severity during a heat wave in the area.

Some alerts 322 b are relatively minor and might, for example, result inthe automatic adjustment of equipment settings by VFM Server 110, or mayrequire user intervention, but not imminently. Other alerts 322 b mayrequire more immediate attention, or may potentially result in moresevere problems, even if they do not require immediate attention. Thenumber of different severity levels is the result of design andengineering tradeoffs that will be apparent to one skilled in the artwithout departing from the spirit of the present invention.

It should be noted that the placement of sensors within a room or areaof the home (or within proximity of particular items of equipment orinfrastructure) can be very important to the relative accuracy of thesensor data with regard to its desired purpose. In one embodiment, theprecise location of each sensor is stored in Home Health Record 126 andused by Alert Generation Engine 350 b in the process of generatingalerts 322 b. In another embodiment, an alert 322 b may include“improper placement” of a sensor, which ultimately may result in anaction (generated by Action and Goals Optimization Engine 350 c)recommending a better location.

As alluded to above, alerts 322 b are often contextual in nature. Forexample, as discussed earlier, Alert Generation Engine 350 b mightdetect abnormally high sump pump activity, but not generate an alert dueto the contextual environmental data that such activity has occurredduring a period of heavy rain. In other contexts, a forecast of heavyrain might result in the generation of an alert 322 b with respect tothe sump pump due to its erratic behavior during prior periods of heavyrain, along with the fact that it has not serviced in the interim.

In one embodiment, users may initiate queries to VFM Server 110 (e.g.,from a voice-enabled mobile app or web browser interface 165 on a laptopor desktop computer). For example, as noted above, a user might submit aquery regarding the relative efficiency of their AC system. Such a querywould effectively trigger an alert and related metadata regarding thesubject of the alert (e.g., the AC system). In this scenario, Action andGoals Optimization Engine 350 c would generate a “response” actionincluding, for example, a summary of the efficiency of the installed ACsystem (based upon its historic performance) and perhaps a comparativerecommendation for an upgrade to a more efficient competitivealternative product.

Providers may also initiate queries to VFM Server 110. For example, awarranty provider may (with a homeowner's permission) initiate queriesregarding the homeowner's maintenance history. As a result of a queryreturning favorable information regarding the homeowner's maintenancerecord, the warranty provider might offer the homeowner a discount on anextended warranty.

In the event Alert Generation Engine 350 a fails to generate an alert(as is likely to be the case during most iterations), then Home HealthRecord 126 is updated and VFM system 100 repeats the process for thenext iteration (i.e., receiving sensor data from Premises Controller 158of each home, etc.). When an alert 322 b is generated, the alert 322 band its associated alert attributes 324 b, along with many of the sameinputs provided to Alert Generation Engine 350 a, are provided as inputsto Action and Goals Optimization Engine 350 c.

Turning to FIG. 3C, diagram 300 c illustrates how, in one embodiment,Action and Goals Optimization Engine 350 c utilizes these inputs 310 c(in this embodiment, including alerts and alert attributes 312 c, scores316 c generated by Scoring Engine 350 a and the same data 318 c storedin Home Health Record 126 and provided to the other prediction engines)to generate a set of outputs 320 c representing recommended actions 322c and corresponding action attributes 324 c.

It is important to emphasize that the actions 322 c generated by Actionand Goals Optimization Engine 350 c are not designed merely to addressthe associated alert 322 b—i.e., the “symptom” of the problem that maybe resolved relatively easily in the course of one iteration, or mayrequire a more complex series of actions not yet fully determined.Rather, each action 322 c generated by Action and Goals OptimizationEngine 350 c is the “next action” to be performed—i.e., the next step ina dynamic iterative troubleshooting process.

In other words, follow-on actions (determined during subsequentiterations) cannot necessarily be determined without additionalinformation, such as the results of the current action 322 c, userand/or provider feedback and the changes that occur in sensor andenvironmental data over time. It is this dynamic feedback approach,often involving more than one related alert-action pair, that provides ameaningful simulation of the ideal Virtual Facilities Manager.

Moreover, it should be noted that related alert-action pairs need notinvolve the same alert. In many cases, a different alert will begenerated during subsequent alerts, although its associated action mayrelate to the same underlying problem. This process effectivelydiagnoses, as well as addresses, that underlying problem (with timelyassistance from homeowners and providers), as shown in the scenariosdiscussed below.

In one embodiment, Action and Goals Optimization Engine 350 c does morethan predict the next action that best corresponds with the currentalert (in the context of the other inputs, including current andhistorical data, operational models, etc.). It also takes into accountvarious User Goals to determine which of various potentially relevantactions optimally furthers one or more of these User Goals.

For example, one homeowner may want to optimize for lowest cost, whileanother might want to optimize for highest energy efficiency. Stillothers might choose to optimize for least inconvenience regardingservice calls and homeowner involvement, or greatest reliability orsafety. Various other User Goals will be apparent to those skilled inthe art.

The User Goals are not considered merely at the level of an individualitem of equipment, but at a higher system or whole-home level (or evenacross a network of homes). For example, the cost of replacing (asopposed to repairing) a relatively expensive component of a piece ofequipment may still outweigh the projected cost of a more expensivefuture replacement of the entire unit (or of other interdependentequipment in the same system). Action and Goals Optimization Engine 350c performs these comparative analyses based upon operational models andrelated financial and other data provided as inputs.

Action and Goals Optimization Engine 350 c also takes into accountsystemic considerations when looking at particular equipment torecommend. For example, additional “smart control” devices may berecommended to provide a homeowner with more control over theoperational parameters of certain equipment, enabling the homeowner witha greater ability to satisfy an energy efficiency User Goal.

Moreover, in another embodiment, homeowners can specify an orderedpriority of combinations of these User Goals, as well as a weighted“optimization function” that has multiple User Goals as parameters. Forexample, a user might prioritize lowest cost, but with a minimal levelof energy efficiency.

As a result of these specified User Goals, Action and Goals OptimizationEngine 350 c effectively selects, from among a domain of potentialactions exceeding a threshold of correlation to the current alert (inthe context of relevant historical data), the particular action thatmost optimally satisfies the User Goals (or function thereof). In short,all other factors being equal, the generated action might differdepending upon a particular homeowner's User Goals.

It should be noted that, while these User Goals provide homeowners withthe advantage of some degree of control over the maintenance of theirpremises, they also provide advantages to providers. For example,homeowners focused on energy efficiency are far more targeted customersfor upgrades to more energy efficient models than typical homeowners. Ingeneral, the better a homeowner's needs and desires can be matched tothe particular products and services offered by certain providers, themore likely a “win-win” outcome can be achieved.

In one embodiment, Action and Goals Optimization Engine 350 c employsthe scores 325 a generated by Scoring Engine 350 a (among other inputs)to generate actions 322 c that facilitate these “win-win” outcomesbetween homeowners and various providers. For example, the integrationof Home Efficiency scores enables “energy utility” providers to bettermanage energy usage by homeowners (e.g., by offering discounts for lowerHome Efficiency scores over time). Similarly, Maintenance scores enableWarranty providers to incentivize more proactive preventive maintenancebehavior over time. And Home Reliability scores enable Insuranceproviders to make much more targeted risk assessments, such as discountsfor higher Home Reliability scores over time.

In the embodiment illustrated in FIG. 3C, Action and Goals OptimizationEngine 350 c generates various different types of actions 322 c. Forexample, many such actions 322 c relate to troubleshooting stepsregarding operational malfunctions of an individual unit of equipment orone or more of its components. An action might involve adjusting asetting, reporting status data that is not available via a sensor,flipping a switch, describing a sound and many other actions designed toyield feedback that will inform subsequent troubleshooting steps.

Moreover, certain actions 322 c will be performed directly by VFM Server110 (e.g., over the Internet 105, via Premises Controller 158 orotherwise). Other actions include steps to be taken by the homeowner,including performing actions and/or providing information. Still othersmay require the intervention of a service provider (including automatednotification of the service provider, scheduling, etc.). And certainactions will simply involve waiting for a subsequent alert (e.g.,waiting a period of time before determining the next step due to interimchanges in sensor and/or environmental data).

Other actions 322 c may relate less to troubleshooting and fixing adiscrete problem and more to ongoing preventive maintenance tasks. Heretoo, certain maintenance tasks may involve configuration procedures thatare performed automatically by VFM Server 110, while others (e.g.,changing a filter) may be performed by homeowners, and still others mayrequire a service provider.

Certain actions 322 c may involve “time of day” recommendations, e.g.,based on learning the optimal time of day to charge an electric car oroperate a pool pump (whether optimized for cost, energy efficiency orother User Goals or combinations thereof). Moreover, systemic factors(such as thermal heat transfer, structural considerations, airflow,etc.) are also taken into account. For example, Action and GoalsOptimization Engine 350 c may recommend that a dryer that gives off asignificant amount of heat be operated in the evening to avoidtriggering air conditioning.

Still other actions 322 c may involve recommendations to purchase adevice or item of equipment or component (to supplement, replace and/orupgrade a unit or component thereof) to address a systemic issue. Forexample, certain components may wear out with relative frequency, andproactive replacement of such components may result in extending thelife of a piece of equipment, or even an entire system (e.g., due to theinterdependencies among the operation of particular items of equipment).Here too, these recommendations are affected by the User Goals, whetherat the level of an individual item of equipment or a system ofequipment, or across the homeowner's entire residence.

As was the case with Alert Generation Engine 350 b, Action and GoalsOptimization Engine 350 c generates not only actions 322 c, but alsocorresponding action attributes 324 c. For example, the actionattributes 324 c identify the particular item(s) of equipment orcomponents to which the action is targeted, as well as quantifieddetails (e.g., a desired setting of an item of equipment, such as athermostat). They also indicate a severity level, giving the homeownerand/or service provider a sense of the degree of urgency in performingthat particular troubleshooting step (and in one embodiment thetimeframe or condition necessary before the next step can bedetermined).

Once Action and Goals Optimization Engine 350 c generates an action 322c (along with action attributes 324 c), the resulting alert-action pairis then processed by VFM Server 110. In one embodiment, CommunicationsAssistant 124 manages the process of communicating alert-action pairs(and related metadata and other summarized historical data from HomeHealth Record 126) to homeowners and/or providers.

In some cases, Communications Assistant 124 may determine that thethreshold for communicating a particular alert-action pair to ahomeowner has not been exceeded. For example, a very minor alert may beassociated with “no action” and merely saved in Home Health Record 126for future iterations. In other cases, the action may be performed byVFM Server 110 without a need even to notify the homeowner. In oneembodiment, the homeowner elects whether to receive notifications forparticular (e.g., “low severity”) alerts and actions.

In the event Communications Assistant 124 notifies the homeowner of thealert-action pair, then the action 322 c will be implemented by VFMServer 110 or the homeowner (depending on the particular alert-actionpair). The homeowner may also provide feedback (e.g., after checking forparticular status data) and even initiate queries to CommunicationsAssistant 124. As noted above, a query will trigger an alert (during asubsequent iteration) and a corresponding “response” action (e.g.,providing information on the status of an item of equipment, a system ora whole-home attribute).

Communications Assistant 124 also notifies homeowners when informationis required. For example, as noted above, Communications Assistant 124may query a homeowner to determine whether a prior suggested action(e.g., changing an air filter) has been implemented.

In some cases, the Communications Assistant 124 will also notify aService Provider 130 or Other Provider 140. For example, a ServiceProvider 130 may elect to be notified only of alerts and/or actions(with respect to a specified group of homeowners 160) exceeding aparticular severity level. But Service Providers 130 may well benotified even when an alert-action pair is not yet recommending actionsrequiring a service call.

In this manner, Service Providers 130 have an automated “up-to-date”summary of the status of a homeowner's premises 160 enabling them to beproactive (e.g., recommending a preventive service call) and, at thevery least, more efficient when the need for a recommended service callarises. In other cases, a Service Provider 130 may perform remotetroubleshooting over the phone or via VFM Server 110 (thereby avoidingthe need for a physical service call).

Similarly, Other Providers (insurance, warranty, institutional owners,etc.) 140 may also receive over the Homecare Network similar summarystatus reports regarding a homeowner's premises 160, including summaryscores and sub-scores providing more systemic information. They too canreach out proactively depending upon the particular situation. Forexample, a warranty provider might offer a homeowner an extendedwarranty based on an above-average maintenance history or relativelystable performance of particular equipment or systems. Or the warrantyprovider might offer a discounted warranty for a new unit based on anindication that a particular item of equipment is nearing its end oflife.

Many other scenarios for proactive maintenance services will be apparentto those skilled in the art, based upon the valuable “health status”data available to a variety of providers over time. In one embodiment,VFM Server 110 summarizes and targets such information (extracted fromHome Health Record 126) for particular types of providers, or even for aspecific provider.

Once the action has been generated by VFM Server 110 (but withoutwaiting for a homeowner or provider to implement such action), HomeHealth Record 126 is updated and the process repeats itself for the nextiteration (i.e., receiving sensor data from Premises Controller 158 ofeach home, etc.). The following scenarios will illustrate relativeadvantages of the VFM architecture in diagnosing and iterativelytroubleshooting particular problems over time.

Scenario #1 AC Unit Problem

In this first scenario, VFM system 100 generates an initial alertindicating that an air conditioning unit (AC unit) is “working too hard”in that It is not achieving its thermostat setting within an expectedperiod of time (based on its operational model). It's power iscontinuous, yet temperature readings over time are not risingsufficiently to reach the thermostat setting.

The recommended action corresponding to this alert is that the homeownerclose a door to a particular room that is a suspected cause of thisproblem. A door sensor enabled VFM system 100 to be aware that thisparticular door had been left open for an extended period of time.

Upon performing this action, no more alerts are generated for awhile,and the action appears to have solved the problem. In other words, theAC unit is reaching its temperature set point as expected (within areasonable tolerance).

After some time has passed (e.g., a few days or weeks), VFM system 100generates a second alert indicating that the AC unit is making anunusual noise (e.g., based upon a nearby sound sensor). Thecorresponding action instructs the homeowner to check for a visibleobstruction (e.g., a tree limb that may have fallen next to the unit).

Upon checking the AC unit, the homeowner finds no visible obstruction,but confirms that the noise is still present. Shortly thereafter, VFMsystem 100 issues a third alert equivalent to the initial alertregarding the AC unit working too hard.

In this case, however, VFM system 100 is aware that the door remainedclosed. Yet, other sensors in proximity to the AC unit (e.g.,intake/outtake temperature sensors and pressure sensors) indicate thatthe freon level of the AC unit is low. So a corresponding action (nowpart of the 3^(rd) related alert-action pair) recommends that coolant(freon) be added.

If the homeowner is capable, VFM system 100 would be configured toinstruct the homeowner to add the coolant. Otherwise, a service call isscheduled (with or without the homeowner's approval, depending upon apredetermined configuration option). In any event, the action iscompleted and the coolant is added. For some period of time thereafter,no further alerts are generated, at least tentatively indicating thatthe problem has been resolved.

However, many months later, VFM system 100 generates the same alert asthe initial alert, indicating that the AC unit is working too hard.Knowing that the coolant had been filled within the past year, VFMsystem 100 suspects a freon leak and generates an action recommending aservice call. A service technician proactively contacts the homeownervia VFM system 100 and schedules the service call.

Because the service technician has been informed of this entire servicehistory over time, the service call is relatively quick, efficient andinexpensive. The freon leak is found and repaired relatively quickly. Nofurther alerts relating to the AC unit are generated for many years, andthe problem appears to have been resolved.

At this point, the homeowner has avoided multiple service calls due toproactive alerts and recommended corresponding actions that not onlyresolved minor symptoms along the way, but eventually resulted in adiagnosis and resolution of a potentially more significant underlyingproblem (freon leak). Once VFM system 100 diagnosed this problem, itaddressed the problem with a single, relatively inexpensive service call(due to the proactive related alert-action pairs generated by VFM system100 over time, as well as the summary of the AC unit's service historydelivered to the service technician over time).

Many years later, however, another alert is generated indicating thatthe AC unit is again working too hard, and that its power consumption isabnormally high. The corresponding recommended action is to replace theair filters (i.e., preventive maintenance), which the homeowner iscapable of doing.

Yet, a short time (e.g., a few days) later, another alert is generatedindicating a relatively loud noise and another potential freon leak. Thecorresponding recommended action at this point is to replace the AC unitwith a new more energy efficient model (after recognizing that theexpense of what it suspects will be a major repair is not justified, andthat the homeowner's User Goals prioritize energy efficiency andreliability over short-term expenses).

Scenario #2 Systemic (System-Wide and Whole-Home) Issues

In this scenario, multiple pieces of equipment are involved, and thehomeowner's premises are located in a hot and humid area with relativelyextreme weather conditions. The homeowner installs both an AC unit and adehumidifier which work together to provide a more comfortable coolingenvironment in the premises, despite the area's hot and humid weather.

Years after the units have been working fine, VFM system 100 generatesan alert indicating that the AC unit is working too hard (e.g., becauseit is 110 degrees Fahrenheit outside, and the AC unit is set to achieve68 degrees). The corresponding recommended action is to raise thethermostat to 71 degrees (on the assumption that such a temperature willstill provide sufficient comfort).

VFM system 100 automatically raises the thermostat to 71 degrees, afternotifying the homeowner and receiving permission in accordance with thepredefined configuration covering such scenarios. Over a relativelyshort period of time, however, the outside temperature increased to 115degrees and VFM system 100 generated the same alert indicating that theAC unit was working too hard.

In this case, the corresponding recommended action was for the homeownerto close the dampers in rooms having relatively low occupancy(determined based upon arrays of strategically placed motion sensors)and to ensure the doors to such rooms remained closed. Nevertheless, VFMsystem 100 soon generated a third alert, in this case indicating thatthe dehumidifier was working too hard (evidenced, for example, by a longruntime to reduce the relative humidity below 70 degrees).

Based on an analysis of the operation of these interdependent units overtime, VFM system 100 generates a corresponding recommended action ofadding a second dehumidifier. In other words, VFM system 100 determinedthat the hot and humid environment was not being sufficiently addressedby the AC unit and single dehumidifier.

It also recognized that a larger AC unit, or a second AC unit, was farless cost effective than adding a second dehumidifier, which as a systemwith the AC unit was projected to provide sufficient cooling andcomfort. The homeowner purchased the second dehumidifier and received noalerts for many years, effectively solving the problem.

After quite a few years, however, VFM system 100 generated an alertindicating that the AC unit was running inefficiently, and noting thatits age indicated that it was nearing the end of its life. Thecorresponding recommended action was to purchase a new smaller but farmore efficient AC unit that would integrate well with the 2dehumidifiers (a cost-effective solution).

Moreover, the recommended action further suggested adding a heatexchanger and ceiling fans in specified bedrooms to complement the newsmaller AC unit. As a result, the homeowner extended the life not onlyof the original AC unit, but of the larger system itself, which nowincluded the new smaller AC unit, two dehumidifiers, a heat exchanger(to remove excess heat from the premises and heat the pool) and ceilingfans in specified bedrooms.

1. A system that maintains the health of the equipment andinfrastructure of one or more properties, the system comprising thefollowing: (a) a data monitor that receives dynamic data reflectingchanges over time in the state of the equipment and infrastructure; (b)an alert engine that, based in part upon the dynamic data, (i) detectsan abnormal condition among one or more characteristics of the equipmentand infrastructure and, in response, (ii) generates an alertrepresenting a symptom of an underlying problem; and (c) an actionengine that generates an action corresponding to an alert, wherein theaction is a troubleshooting step designed to address the underlyingproblem of which the corresponding alert is a symptom, and wherein eachalert and corresponding action together constitute an alert-action pair;(d) wherein the alert engine and action engine together generate overtime a plurality of related alert-action pairs designed to address thesame underlying problem.
 2. The system of claim 1, wherein at least oneaction of one of the plurality of related alert-action pairs isperformed automatically by the system.
 3. The system of claim 1, whereinat least one action of one of the plurality of related alert-actionpairs is performed by an owner of one of the one or more properties. 4.The system of claim 1, further comprising a service provider networkcomprising a plurality of service providers with relevant expertise inservicing one or more items of the equipment and infrastructure, whereinone of the plurality of service providers communicates troubleshootinginput to the system representing an action corresponding to an alert. 5.The system of claim 1, further comprising a home health recordcomprising a set of static and dynamic information reflecting the stateof one or more components of the equipment and infrastructure of one ofthe one or more properties, wherein the home health record includes oneor more scores quantifying the systemic state of that property, andwherein one of the one or more scores improves as a result of theplurality of related alert-action pairs.
 6. A method for maintaining thehealth of the equipment and infrastructure of one or more properties,the method comprising the following steps: (a) receiving dynamic datareflecting changes over time in the state of the equipment andinfrastructure; (b) detecting, based in part upon the dynamic data, anabnormal condition among one or more characteristics of the equipmentand infrastructure and, in response, generating an alert representing asymptom of an underlying problem; (c) generating an action correspondingto an alert, wherein the action is a troubleshooting step designed toaddress the underlying problem of which the corresponding alert is asymptom, and wherein each alert and corresponding action togetherconstitute an alert-action pair; and (d) generating over time aplurality of related alert-action pairs designed to address the sameunderlying problem.
 7. The method of claim 6, wherein at least oneaction of one of the plurality of related alert-action pairs isperformed automatically.
 8. The method of claim 6, wherein at least oneaction of one of the plurality of related alert-action pairs isperformed by an owner of one of the one or more properties.
 9. Themethod of claim 6, further comprising the step of integrating a serviceprovider network comprising a plurality of service providers withrelevant expertise in servicing one or more items of the equipment andinfrastructure, wherein one of the plurality of service providerscommunicates troubleshooting input representing an action correspondingto an alert.
 10. The method of claim 6, further comprising the step ofmaintaining a home health record comprising a set of static and dynamicinformation reflecting the state of one or more components of theequipment and infrastructure of one of the one or more properties,wherein the home health record includes one or more scores quantifyingthe systemic state of that property, and wherein one of the one or morescores improves as a result of the plurality of related alert-actionpairs.
 11. A system that maintains the health of the equipment andinfrastructure of one or more properties, the system comprising thefollowing: (a) a home health record comprising a set of static anddynamic data, including historical data, reflecting the state of one ormore items of the equipment and infrastructure; (b) an alert enginethat, based in part upon the dynamic data, (i) detects an abnormalcondition among one or more characteristics of the equipment andinfrastructure and, in response, (ii) generates an alert representing asymptom of an underlying problem; (c) a communications assistant thatmanages communications with a service provider network, the serviceprovider network comprising a plurality of service providers withrelevant expertise in servicing one or more items of the equipment andinfrastructure; and (d) a service provider manager that, in response tothe generation of an alert, (i) generates relevant summary data from thehome health record to facilitate troubleshooting of the underlyingproblem of which the alert is a symptom, (ii) identifies a subset of theplurality of service providers with specific expertise introubleshooting that underlying problem, (iii) provides the relevantsummary data to the identified subset of the plurality of serviceproviders via the service provider network; and, in response, (iv)receives from one of the identified subset of the plurality of serviceproviders, via the service provider network, a recommended actionrepresenting a step in troubleshooting that underlying problem.
 12. Thesystem of claim 11, wherein the recommended action is performedautomatically.
 13. The system of claim 11, wherein the recommendedaction is performed by an owner of one of the one or more properties.14. The system of claim 1, further comprising a home health recordcomprising a set of static and dynamic information reflecting the stateof one or more components of the equipment and infrastructure of one ofthe one or more properties, wherein the home health record includes oneor more scores quantifying the systemic state of that property, andwherein one of the one or more scores improves as a result of therecommended action.
 15. A method for maintaining the health of theequipment and infrastructure of one or more properties, the methodcomprising the following steps: (a) storing a set of static and dynamicdata, including historical data, reflecting the state of one or moreitems of the equipment and infrastructure; (b) detecting, based in partupon the dynamic data, an abnormal condition among one or morecharacteristics of the equipment and infrastructure and, in response,generating an alert representing a symptom of an underlying problem; and(c) managing communications with a service provider network, the serviceprovider network comprising a plurality of service providers withrelevant expertise in servicing one or more items of the equipment andinfrastructure, by performing the following steps in response to thegeneration of an alert: (i) generating relevant summary data from thestored set of static and dynamic data to facilitate troubleshooting ofthe underlying problem of which the alert is a symptom, (ii) identifyinga subset of the plurality of service providers with specific expertisein troubleshooting that underlying problem, (iii) providing the relevantsummary data to the identified subset of the plurality of serviceproviders via the service provider network, and, in response, (iv)receiving from one of the identified subset of the plurality of serviceproviders, via the service provider network, a recommended actionrepresenting a step in troubleshooting that underlying problem.
 16. Thesystem of claim 15, wherein the recommended action is performedautomatically.
 17. The system of claim 15, wherein the recommendedaction is performed by an owner of one of the one or more properties.18. The system of claim 15, further comprising the step of maintaining ahome health record comprising a set of static and dynamic informationreflecting the state of one or more components of the equipment andinfrastructure of one of the one or more properties, wherein the homehealth record includes one or more scores quantifying the systemic stateof that property, and wherein one of the one or more scores improves asa result of the recommended action.
 19. A system that maintains thehealth status of the equipment and infrastructure of a plurality ofproperties, comprising: (a) a home health record comprising a set ofstatic and dynamic information reflecting the state of one or morecomponents of the equipment and infrastructure of each of the pluralityof properties, including one or more scores quantifying the systemicstate of each of the plurality of properties; (b) a home health recordgenerator that maintains the health status of each of the plurality ofproperties by modifying the scores as the state of one or morecomponents of the home health record changes over time; (c) an alertengine that generates an alert when one or more scores of the homehealth record indicate the existence of an abnormal condition in one ofthe plurality of properties; and (d) an action engine that generates anaction to address the alert.
 20. The system of claim 19, wherein theaction is performed automatically by the system.
 21. The system of claim19, wherein the action is performed by an owner of one of the pluralityof properties.
 22. The system of claim 19, further comprising a serviceprovider network comprising a plurality of service providers withrelevant expertise in servicing one or more items of the equipment andinfrastructure, wherein one of the plurality of service providerscommunicates troubleshooting input to the system representing an actioncorresponding to an alert.
 23. The system of claim 19, wherein thestatic information includes identification and room location ofindividual items of equipment.
 24. The system of claim 19, wherein thedynamic information includes sensor data reflecting the state of one ormore items of equipment.
 25. The system of claim 19, wherein the dynamicinformation includes attributes of prior alerts and correspondingactions.
 26. The system of claim 19, wherein the home health recordincludes environmental data external to each of the plurality ofproperties.
 27. The system of claim 19, wherein the systemic states of aproperty include one or more of the following: reliability, efficiency,safety, maintenance, fire risk, flood risk and air quality.